scholarly journals Evaluation of Applicability of Various Color Space Techniques of UAV Images for Evaluating Cool Roof Performance

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4213
Author(s):  
Kirim Lee ◽  
Jihoon Seong ◽  
Youkyung Han ◽  
Won Hee Lee
Keyword(s):  
Surface Temperature ◽  
Pearson Correlation ◽  
Color Space ◽  
Thermal Infrared ◽  
Urban Heat Islands ◽  
Infrared Sensor ◽  
Indoor Temperature ◽  
Cool Roof ◽  
Urban Heat ◽  
Infrared Cameras

Global warming is intensifying worldwide, and urban heat islands are occurring as urbanization progresses. The cool roof method is one alternative for reducing the urban heat island phenomenon and lowering the heat on building roofs for a comfortable indoor environment. In this study, a cool roof evaluation was performed using an unmanned aerial vehicle (UAV) and a red, green and blue (RGB) camera instead of a laser thermometer and a thermal infrared sensor to evaluate existing cool roofs. When using a UAV, an RGB sensor is used instead of expensive infrared sensor. Various color space techniques, namely light-reflectance value, hue saturation value (HSV), hue saturation lightness, and YUV (luma component (Y) and two chrominance components, called U (blue projection) and V (red projection)) derived from RGB images, are applied to evaluate color space techniques suitable for cool roof evaluation. This case study shows the following quantitative results: among various color space techniques investigated herein, the white roof with lowest temperature (average surface temperature: 44.1 °C; average indoor temperature: 33.3 °C) showed highest HSV, while the black roof with the highest temperature (surface temperature average: 73.4 °C; indoor temperature average: 37.1 °C) depicted the lowest HSV. In addition, the HSV showed the highest correlation in both the Pearson correlation coefficient and the linear regression analyses when the correlation among the brightness, surface temperature, and indoor temperature of the four color space techniques was analyzed. This study is considered a valuable reference for using RGB cameras and HSV color space techniques, instead of expensive thermal infrared cameras, when evaluating cool roof performance.

scholarly journals Roof Color-Based Warm Roof Evaluation in Cold Regions Using a UAV Mounted Thermal Infrared Imaging Camera

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6488
Author(s):  
Kirim Lee ◽  
Jinhwan Park ◽  
Sejung Jung ◽  
Wonhee Lee
Keyword(s):  
Surface Temperature ◽  
Infrared Imaging ◽  
Pearson Correlation ◽  
Thermal Infrared ◽  
Thermal Infrared Imaging ◽  
Cool Roof ◽  
Color Differences ◽  
Aerial Vehicle ◽  
Heating Load ◽  
Objective Basis

Existing studies on reducing urban heat island phenomenon and building temperature have been actively conducted; however, studies on investigating the warm roof phenomenon to in-crease the temperature of buildings are insufficient. A cool roof is required in a high-temperature region, while a warm roof is needed in a low-temperature or cold region. Therefore, a warm roof evaluation was conducted in this study using the roof color (black, blue, green, gray, and white), which is relatively easier to install and maintain compared to conventional insulation materials and double walls. A remote sensing method via an unmanned aerial vehicle (UAV)-mounted thermal infrared (TIR) camera was employed. For warm roof evaluation, the accuracy of the TIR camera was verified by comparing it with a laser thermometer, and the correlation between the surface temperature and the room temperature was also confirmed using Pearson correlation. The results showed significant surface temperature differences ranging from 8 °C to 28 °C between the black-colored roof and the other colored roofs and indoor temperature differences from 1 °C to 7 °C. Through this study, it was possible to know the most effective color for a warm roof according to the color differences. This study gave us an idea of which color would work best for a warm roof, as well as the temperature differences from other colors. We believe that the results of this study will be helpful in heating load research, providing an objective basis for determining whether a warm roof is applied.

scholarly journals Land Surface Temperature Retrieval from LANDSAT-8 Thermal Infrared Sensor Data and Validation with Infrared Thermometer Camera

2018 ◽  
Vol 7 (4.20) ◽  
pp. 608 ◽  
Author(s):  
Muhammad Mejbel Salih ◽  
Oday Zakariya Jasim ◽  
Khalid I. Hassoon ◽  
Aysar Jameel Abdalkadhum
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Infrared Sensor ◽  
Infrared Thermometer ◽  
Ground Truth Data ◽  
Land Cover Types

This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LANDSAT-8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the measurements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the imaged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measurement taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LANDSAT-8 data to specify temperature differences in land cover and compare the relationship between land surface temperature and land cover types.   

scholarly journals Night Thermal Unmixing for the Study of Microscale Surface Urban Heat Islands with TRISHNA-Like Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1449 ◽  
Author(s):  
Carlos Granero-Belinchon ◽  
Aurelie Michel ◽  
Jean-Pierre Lagouarde ◽  
Jose A. Sobrino ◽  
Xavier Briottet
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Image Resolution ◽  
Urban Heat Islands ◽  
Land Uses ◽  
Regression Kriging ◽  
Heat Islands ◽  
Urban Heat ◽  
Land Covers ◽  
The City

Urban Heat Islands (UHIs) at the surface and canopy levels are major issues in urban planification and development. For this reason, the comprehension and quantification of the influence that the different land-uses/land-covers have on UHIs is of particular importance. In order to perform a detailed thermal characterisation of the city, measures covering the whole scenario (city and surroundings) and with a recurrent revisit are needed. In addition, a resolution of tens of meters is needed to characterise the urban heterogeneities. Spaceborne remote sensing meets the first and the second requirements but the Land Surface Temperature (LST) resolutions remain too rough compared to the urban object scale. Thermal unmixing techniques have been developed in recent years, allowing LST images during day at the desired scales. However, while LST gives information of surface urban heat islands (SUHIs), canopy UHIs and SUHIs are more correlated during the night, hence the development of thermal unmixing methods for night LSTs is necessary. This article proposes to adapt four empirical unmixing methods of the literature, Disaggregation of radiometric surface Temperature (DisTrad), High-resolution Urban Thermal Sharpener (HUTS), Area-To-Point Regression Kriging (ATPRK), and Adaptive Area-To-Point Regression Kriging (AATPRK), to unmix night LSTs. These methods are based on given relationships between LST and reflective indices, and on invariance hypotheses of these relationships across resolutions. Then, a comparative study of the performances of the different techniques is carried out on TRISHNA synthesized images of Madrid. Since TRISHNA is a mission in preparation, the synthesis of the images has been done according to the planned specification of the satellite and from initial Aircraft Hyperspectral Scanner (AHS) data of the city obtained during the DESIREX 2008 capaign. Thus, the coarse initial resolution is 60 m and the finer post-unmixing one is 20 m. In this article, we show that: (1) AATPRK is the most performant unmixing technique when applied on night LST, with the other three techniques being undesirable for night applications at TRISHNA resolutions. This can be explained by the local application of AATPRK. (2) ATPRK and DisTrad do not improve significantly the LST image resolution. (3) HUTS, which depends on albedo measures, misestimates the LST, leading to the worst temperature unmixing. (4) The two main factors explaining the obtained performances are the local/global application of the method and the reflective indices used in the LST-index relationship.

scholarly journals An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

2015 ◽  
Vol 7 (4) ◽  
pp. 4268-4289 ◽  
Author(s):  
Fei Wang ◽  
Zhihao Qin ◽  
Caiying Song ◽  
Lili Tu ◽  
Arnon Karnieli ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Sensor Data ◽  
Landsat 8 ◽  
Infrared Sensor ◽  
Temperature Retrieval

scholarly journals Relationship between the distribution of aquatic plants and the water surface temperature using UAV with thermal infrared sensor.

2020 ◽  
Vol 22 (2) ◽  
pp. 155-163
Author(s):  
Hideyuki NIWA ◽  
Kosuke IMAI ◽  
Syunsuke SUZUKI ◽  
Ryou SHIMIZU ◽  
Shigeharu KOGUSHI
Keyword(s):  
Surface Temperature ◽  
Aquatic Plants ◽  
Water Surface ◽  
Thermal Infrared ◽  
Infrared Sensor ◽  
Water Surface Temperature

scholarly journals Spatial modelling of urban heat islands and its planning implications in Obio/Akpor local government area

2021 ◽  
Vol 5 (5) ◽  
pp. 240-250
Author(s):  
Lawson Nwidum ◽  
Kurotamuno Peace Jackson ◽  
Ibama Brown
Keyword(s):  
Local Government ◽  
Surface Temperature ◽  
Local Government Area ◽  
Urban Heat Islands ◽  
Tree Planting ◽  
Urban Surface ◽  
Spectral Radiance ◽  
Electric Cars ◽  
Urban Heat ◽  
The Impact

Urban Heat Island (UHI) has become a global recurring phenomenon in most urban centres. Obio/Akpor Local Government Area has had a fair share of this phenomenon owing to its thriving trend in both planned and unplanned urbanisations. The study looks at the impact of UHI in selected communities in Obio/Akpor Local Government in five epochs of 2000, 2005, 2010, 2015 and 2020. Parts of the objectives include identifying the UHI in these communities in the Local Government Area, modelling of UHI in selected communities in Obio/Akpor Local Government Area and determining the trend in UHI using Epoch data of Urban Surface Temperature from LANDSAT thermal imageries Figure 1. The study adopted Thermal Infrared Remote (TIR) Sensing and Geospatial Information System (GIS) Techniques using LANDSAT TM, LANDSAT ETM and LANDSAT OLI sensors to acquire Urban Surface temperature data emitted by objects in the study area and store the information as a digital number (DN) thermal band (B6, B61 and B10) as well as secondary data acquired from the Nigerian Meteorological Agency (NIMET). Urban Surface Temperature was obtained through the following processes: Acquisition of Urban Surface Temperature value of the study area in form of DN, the conversion of DN to Spectral radiance using the Spectral radiance equation. The data were processed, analysed, and modelled using ESRI’s ArcGIS 10.1. The results revealed that in 2000, the Average Urban Temperature of the study area was 23.480°C, the value increase to 27.647°C in 2005 with a difference of 4.167°C. The temperature of 2005 increased to 31.598°C in 2010 with a difference in temperature of 3.951°C. Accordingly, the temperature of 2010 increased to 33.054°C in 2015 with a temperature difference of 1.456°C and temperature of 2015 increased to 33.070°C with a difference of 0.016°C. The analysis shows an increasing trend of 40% in the Urban Surface Temperature in the study area in the various years under investigation. The study recommends that development should be extended to other Local Government Areas in the state to reduce rural-urban migration to Obio/Akpor Local Government. Tree planting should be encouraged as a way of mitigating the effect of air pollution, heatwaves and harmful gases emitted into the environment by combust engines and gas flaring, the use of combustion engines be replaced by electric cars to reduce the level of carbon dioxide (CO2) emitted ti environment. Policymakers to restrict unplanned urban growth and to increase tree planting in the built-up areas.

scholarly journals Evaluating the Operational Potential of LRV Signatures Derived from UAV Imagery in Performance Evaluation of Cool Roofs

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2787 ◽  
Author(s):  
Park ◽  
Ryu ◽  
Choi ◽  
Um
Keyword(s):  
Surface Temperature ◽  
Pearson Correlation ◽  
Decisive Role ◽  
Cool Roof ◽  
Heating And Cooling ◽  
Objective Evidence ◽  
Strong Negative Association ◽  
Tangible Evidence ◽  
Heating And Cooling Load ◽  
Cool Roofs

It is quite difficult to find studies regarding area-wide data from UAV (Unmanned Aerial Vehicle) remote sensing in evaluating the energy saving performance of a cool roof. Acknowledging these constraints, we investigated whether LRV (Light Reflectance Value) signatures derived from UAV imagery could be used effectively as an indicator of area-wide heating and cooling load that distinctively appears according to rooftop color. The case study provides some quantitative tangible evidence for two distinct colors: A whitish color roof appears near the edge of the highest LRV (91.36) and with a low temperature (rooftop surface temperature: (38.03 °C), while a blackish color roof shows the lowest LRV (18.14) with a very high temperature (65.03 °C) where solar radiation is extensively absorbed. A strong negative association (Pearson correlation coefficient, r = −0.76) was observed between the LRV and surface temperature, implying that a higher LRV (e.g., a white color) plays a decisive role in lowering the surface temperature. This research can be used as a valuable reference introducing LRV in evaluating the thermal performance of rooftop color as rooftops satisfying the requirement of a cool roof (reflecting 75% or more of incoming solar energy) are identified based on area-wide objective evidence from UAV imagery.

scholarly journals Quantifying the effect of waterways and green areas on the surface temperature

2017 ◽  
Vol 39 (1) ◽  
pp. 89 ◽  
Author(s):  
Elis Dener Lima Alves
Keyword(s):  
Surface Temperature ◽  
Temporal Variability ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Natural Area ◽  
Heat Islands ◽  
Green Areas ◽  
Urban Heat ◽  
Cooling Effects ◽  
The Relationship

The cooling effects of urban parks and green areas, which form the “Park Cool Island” (PCI) can help decrease the surface temperature and mitigate the effects of urban heat islands (UHI). Therefore, the objective of this research was to know the temporal variability of PCI intensity, as well as analyze the factors that determines it and propose an equation to predict the PCI intensity in Iporá, Goiás State, Brazil. To this purpose, the PCI intensity values were obtained using the Landsat-8 satellite (band 10), and then correlated with the NDVI and the LAI, in which proposes equations through multiple linear regression to estimate the PCI intensity. The results indicated that: 1) the greater the distance of the natural area, greater the surface temperature; 2) there is a great seasonality in PCI, in which the intensity of PCI is much higher in the spring (or close to it); 3) the relationship between NDVI and LAI variables, showed good coefficients of determination; 4) the equations for the buffer of 200 and 500 m, had low RMSE with high coefficients of determination (r2 = 0.924 and r2 = 0.957 respectively). 

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